Key points analysis of thread pool

Key points analysis of thread pool

1. Consider establishing the number of threads

2. The status of the thread pool

    With open (' flog ') as F:
        F.write ()

3. Close the thread

Instance (simple instance)

Principle:

1: Create 10 thread objects
2:queue the queue to fetch the thread. Available: take; otherwise: wait
3: Thread execution is complete and returned to the thread pool

#!/usr/bin/env python# coding:utf-8import  queueimport Threading "" "1: Create 10 thread objects 2:queue queue fetch thread. Available: take; otherwise: Wait 3: Thread execution complete, return to thread Pool "" "Class ThreadPool (object):    # Set a default parameter Max_num    def __init__ (self,max_num=20):        # Create        a queue Self.queue = Queue.queue (max_num)        # Loop Create 1 objects for        i in Xrange (max_num):            self.queue.put ( Threading. Thread)    def get_thread (self):        return Self.queue.get ()    # Use the class    def add_thread (self):        Self.queue.put (Threading. Thread) # Create object # create 1 queue with up to 10 threads pool = ThreadPool (+) def func (arg,p):    print arg import time    time.sleep ( 2)    P.add_thread () # 300 tasks to execute for i in xrange:    # Gets a thread, thread    = Pool.get_thread ()    # executes this thread    t = Thread (target=func,args= (i,pool))    T.start ()

Example (Twisted.python.threadpool)

#!/usr/bin/env python# coding:utf-8import  contextlibdoing = [] @contextlib. Contextmanagerdef Show (Li,item):    # print "Before"    doing.append (item)    yield    doing.remove (item) print Len (doing) with show (doing,1):    print ' with '    print len (doing) print Len (doing)

Key points analysis of thread pool